Characteristics Of Extreme Precipitation Research Articles

Performance comparison of GPM IMERG V07 with its predecessor V06 and its application in extreme precipitation clustering over Türkiye

Defining regions with similar characteristics for extreme precipitation is crucial for understanding the impacts of climate change, planning and managing water resources, and designing hydraulic structures. However, studies on the regionalization of extreme precipitation for Türkiye are limited, and regional extreme precipitation characteristics are not well defined. In this study, motivated by the need to contribute to this field, homogenous regions for extreme precipitation across Türkiye were determined using the latest version (V07) of Integrated Multi-satellitE Retrievals for GPM (IMERG). We initially validated IMERG V07 estimates using data from 214 ground-based stations and compared the results with its predecessor V06. The results revealed that IMERG showed some notable improvements from V06 to V07 for all seasons, especially in winter. During this season, the correlation coefficient increased from 0.57 to 0.64, the mean absolute bias decreased from 78.22 % to 69.27 %, and the RMSE decreased from 11.10 mm/day to 9.70 mm/day. In V07, while the trend of decreasing accuracy with increasing elevation observed in V06 continues, it has been shown that some notable improvements were achieved in continuous and categorical metrics. We then applied widely used non-hierarchical (K-means) and hierarchical (Ward's method) clustering techniques. To perform this, we first applied Principal Component Analysis (PCA) to reduce the number of variables related to extreme precipitation (e.g. amount, frequency, standard deviation, and seasonality) and geographic characteristics to identify the most significant variables for analysis. The K-means method delineated Türkiye into eight extreme precipitation regions, while the Ward's method resulted in six distinct extreme precipitation regions. We evaluated the results based on the existing extreme precipitation climatology literature for Türkiye and by associating them to known precipitation dynamics, and as a result, we recommended eight precipitation regions determined by the K-means. The identified precipitation regions are expected to contribute to future studies analyzing the effects of climate change and to regional studies on natural disasters resulting from extreme precipitation.

Future Increase in Extreme Precipitation: Historical Data Analysis and Influential Factors

With global warming driving an increase in extreme precipitation, the ensuing disasters present an unsustainable scenario for humanity. Consequently, understanding the characteristics of extreme precipitation has become paramount. Analyzing observational data from 1961 to 2020 across 29 meteorological stations in Heilongjiang Province, China, we employed kriging interpolation, the trend-free pre-whitening Mann–Kendall (TFPW–MK) method, and linear trend analysis. These methods allowed us to effectively assess the spatiotemporal features of extreme precipitation. Furthermore, Pearson’s correlation analysis explored the relationship between extreme precipitation indices (EPIs) and geographic factors, while the geodetector quantified the impacts of climate teleconnections. The results revealed the following: (1) There has been a clear trend in increasing extreme precipitation over the last few decades, particularly in the indices of wet day precipitation (PRCPTOT), very wet day precipitation (R95P), and extremely wet day precipitation (R99P), with regional mean trends of 10.4 mm/decade, 5.7 mm/decade, and 3.4 mm/decade, respectively. This spatial trend showed a decrease from south to north. (2) Significant upward trends were observed in both spring and winter for the maximum 1-day precipitation (RX1day) and the maximum 5-day precipitation (RX5day). (3) The latitude and longitude were significantly correlated with the most extreme precipitation indices, while elevation showed a weaker correlation. (4) Extreme precipitation exhibited a nonlinear response to large-scale climate teleconnections, with the combined influence of factors having a greater impact than individual factors. This research provides critical insights into the spatiotemporal dynamics of extreme precipitation, guiding the development of targeted strategies to mitigate risks and enhance resilience. It offers essential support for addressing regional climate challenges and promoting agricultural development in Heilongjiang Province.

Spatiotemporal Variation Characteristics of Extreme Precipitation in the Mid–Lower Reaches of the Yangtze River Basin Based on Precipitation Events

In addition to greater precipitation on extreme days of precipitation, preceding and succeeding precipitation (PSP) is often an objective component of flooding in the mid–lower reaches of the Yangtze River Basin (MLRYRB). In this study, focused on the temporal distribution pattern of precipitation, the concept of an extreme precipitation event (EPE), defined as a consecutive precipitation event having at least one daily precipitation extreme, is proposed to consider PSP in an extreme event. We analyzed the spatiotemporal variation of four types of EPEs based on daily data obtained from 130 monitoring stations covering 1960–2019. Extreme precipitation increased significantly over the last 60 years (p < 0.01). The frequency and precipitation amount of single-day EPEs accounted for only 13% and 21%, respectively, while multi-day continuous EPE types that are associated with PSP accounted for 87% and 79%, respectively, confirming the connotations of EPEs. The front and late EPEs under the 100-year return level reached 250 mm and 230 mm, respectively. Furthermore, climate warming could lead to significant increases in the frequency of single-day and late EPEs, particularly in the southern region. The EPE concept may be helpful in exploring disaster-causing processes under extreme weather, and it provides a theoretical basis for deriving the precipitation hazard chain, which is more applicable to basins with long precipitation durations.

Characteristics of extreme hourly precipitation induced by tropical cyclones in Zhejiang, China: A comparative analysis based on two different datasets

This study investigates the characteristics of tropical cyclones (TCs) that cause extreme hourly precipitation (EXHP) in Zhejiang, China, using datasets from 67 national stations (Con-ST) and 1551 surface stations (All-ST), spanning 1973–2020 and 2011–2020, respectively. Our analysis revealed notable variations in the EXHP caused by individual TCs. The top 10 % of TCs contributed 37.2–38 % of the total TC-induced EXHP amount, while the bottom 50 % only accounted for 5.8–22.7 %. Using sparse stations may overestimate the impact of TCs causing EXHP in the lower to middle rankings. Long-term trend analysis suggested a consistent increase in TC-induced EXHP despite a decreasing trend in the number of TCs affecting Zhejiang. High-value EXHP centers tended to be concentrated in mountainous areas within 0–50 km from the coastline, where Con-ST stations are sparse and can significantly underestimate EXHP. The maximum amount of EXHP per TC increased logarithmically with the number of observation stations. In Zhejiang, EXHP predominantly occurred northeast of the TC centers (50.4–61.7 %) and within 500 km from the TC center (69.5–81.5 %). High-frequency EXHP centers exhibited a clockwise rotation with increasing distance from the TC center–a pattern resembling a “spiral rainband.” A key area where most EXHP in Zhejiang occurred when TC centers remained within this area was identified. The TC intensity within the key area was a key factor influencing EXHP occurrence in Zhejiang, whereas the duration of TC center residence within the key area and the distance of its movement were crucial predictors of significant EXHP occurrence. The analysis of large-scale environmental fields revealed that high-EXHP TCs are characterized by strong upper-level divergence, intense upward motion, and an expanded subtropical high. The southeasterly steering airflow drives these TCs northwestward, prolonging their impact on Zhejiang and resulting in significant moisture accumulation and EXHP.

Spatial and Temporal Variations’ Characteristics of Extreme Precipitation and Temperature in Jialing River Basin—Implications of Atmospheric Large-Scale Circulation Patterns

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks.

Climatic characteristics of centennial and extreme precipitation in Hangzhou, China

The precipitation characteristics in Hangzhou of Zhejiang Province, China under the background of global climate change are analyzed using the meteorological observation data obtained from the Hangzhou base station in this study. We investigate the climate characteristics of precipitation in Hangzhou from several aspects, such as centennial trend, seasonal change, periodicity and the variation of extreme precipitation. Our results show a linear decreasing trend and obvious interdecadal characteristics in the precipitation of Hangzhou on a centennial timescale. Significantly increased amplitude of precipitation fluctuation was observed since the beginning of the 21st century. For the interdecadal variation of seasonal precipitation on a centennial timescale, precipitation in autumn showed a decreasing trend of 8.1 mm/10a, whereas the trends for the other three seasons were statistically insignificant. The precipitation in Hangzhou showed a decreasing trend in spring and an increasing trend in winter over the past 30 years. Our analyses reveal distinct precipitation cycles, including a quasi-30-year cycle since the 1960s and a quasi-10-year cycle since the 1980s. However, the periodicity has weakened in the past 10 years. In addition, the occurrence of torrential rain has increased rapidly in the past 10 years. Furthermore, influenced by global climate change and regional processes, the variation of extreme precipitation in Hangzhou has changed, which shows strong correlations with the overall trend of annual precipitation. The annual maximum daily precipitation in Hangzhou was mainly in the range of 0–40 mm from 1951 to 1980 and in the range of 40–80 mm from 1981 to 2010 with the maximum daily precipitation occurrence rate of 4.7 times/10a and 6.3 times/10a, respectively. This study emphasizes the risk of urban waterlogging caused by short-term heavy rainfall and provides useful reference to the assessment of extreme meteorological and hydrological disaster risk in Hangzhou.

Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM

The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95<sup>th</sup> percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R<sup>2 </sup>>0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.

Changes in the spatial variability of extreme precipitation characteristics across Peninsular India

Through a comprehensive analysis, this study portrays the changing spatial variability of extreme precipitation characteristics as a consequence of a gradually warming climate in peninsular India. In particular, it emphasizes the coastal areas that are under increased exposure to frequent extreme events in the recent past. Different extreme precipitation characteristics are considered, and the change points are identified based on their trend, mean and standard deviation. Changes in the spatiotemporal variability of extreme precipitation characteristics are identified through empirical orthogonal functions (EOFs). Our findings illustrate the occurrence of discernible changes almost all over the region with varying time points (1970 to 2011), and the extremes with higher thresholds exhibit more prominent changes. More importantly, a notable disparity in extreme indices expressing intensity is observed between the eastern and western coastal regions: change points for the eastern coastal areas (the Bay of Bengal side) predominantly emerged in the post-1980s, in contrast to the pre-1980s points across the western coastal (the Arabian Sea side) regions. Furthermore, after 2001, the spatial coverage of the western region notably expanded, as indicated by a significant increase in wet extremes, including those at the southernmost tip of India. Concurrently, extreme dry events significantly decreased across most of southern India during this period. On the other hand, the intensification of precipitation has become more prominent towards the Bay of Bengal side than towards the Arabian Sea side. This may be attributed to the increased cyclonic activity in the Bay of Bengal. Overall, the findings of this study will aid in understanding the evolving spatial pattern of extreme precipitation indices and will contribute to better management of extreme events and related hazards across peninsular India.

Intensified response of extreme precipitation to rising temperature over the Tibetan Plateau from CMIP6 multi-model ensembles

This study conducted bias correction of the high-performing general circulation model (GCM) data in combination with observed data based on the latest GCM data released by the Coupled Model Intercomparison Project Phase 6 (CMIP6). The ability of GCMs to simulate the characteristics of extreme precipitation over the Tibetan Plateau (TP) was systematically assessed, and the CMIP6 multi-model ensemble was used to accurately project extreme precipitation. Furthermore, the response of extreme precipitation over the TP to global warming was explored. The results demonstrated that daily translation efficiently addressed the issue of apparent overestimation in the model’s raw output, and CMIP6 multi-model ensemble mean exhibited better simulation performance for all extreme precipitation indices over the TP. Additionally, the simulation performance of each index exhibited a divergence across various elevation bands as altitude increased, with the best and worst performance observed at the altitude bands of 1500–2000 m and above 5000 m, respectively. For future changes in extreme precipitation, the indices representing the amount and intensity of extreme precipitation increase significantly, whereas the indices representing the duration of precipitation decrease. Moreover, the plateau will show a spatial pattern of “wet regions becoming dry and dry regions becoming wet”. The relationship between extreme precipitation and temperature follows a positive and peak pattern over most areas of the plateau, while the response of extreme precipitation to temperature will increase by 0.6 %–52.6 % with rising temperature. The present results have important implications for investigating the impacts of climate change on the water cycle in alpine regions.

Characterisation of extreme precipitation changes in the upper reaches of the Yangtze River, China

ABSTRACT The paper analyses the spatial and temporal characteristics of extreme precipitation in the upper reaches of the Yangtze River through extreme precipitation indicators based on the trend method, the Mann–Kendall trend test, and the rescaled extreme deviation extreme deviation using daily precipitation data from 1961 to 2021. The following conclusions were obtained: The overall precipitation in the upper reaches of the Yangtze River is reduced, and the number of rainy days is reduced. The frequency of extreme precipitation is generally reduced, but the spatial difference in the intensity of extreme precipitation is greater, which makes the occurrence of extreme precipitation more concentrated and more destructive. Extreme precipitation indicators showed relatively large fluctuations after 2000, especially in terms of extreme precipitation intensity. The frequency of extreme precipitation in the upper reaches of the Yangtze River is the highest in the main stream of the Yangtze River Basin and the Wujiang River Basin, the intensity of extreme precipitation is in the Jialing River and the Wujiang River Basin, and the accumulation of extreme precipitation is the highest in the Jialing River and the Wujiang River Basin, whereas the maximum value of the station extreme precipitation intensity and frequency is in the Minjiang River Basin.

Impacts of extreme precipitation on water conservation in Beijiang River Basin, China

Water conservation, which has been regarded as one of the most crucial ecosystem service functions of river basins, is a key factor for sustainable development, that has garnered widespread attention in the context of climate change. However, little attention has been paid to daily water conservation and its response to extreme precipitation events. In this study, we selected the Beijiang River Basin (BJRB) as the study area, which is an important ecological barrier for the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), based on the distributed hydrological model WEP-L (Water and Energy transfer Processes in Large River basin) and water balance equation, the spatiotemporal characteristics of extreme precipitation and four daily water conservation indicators (maximum 1 day, maximum 3 day, maximum 5 day, maximum 7 day water conservation) in the BJRB from 1965 to 2009 were analysed, and the responses of these indicators to extreme precipitation were investigated. The results reveal that (1) the amount (RX1day, RX3day, RX5day, RX7day, R95p, and R99p), intensity (SDII), and frequency (R50mm) of extreme precipitation increased in the BJRB during 1965–2009, with SDII undergoing a significant increase. (2) The four water conservation indicators were positive, indicating that the watershed reduced peak flow during the extreme precipitation period by accumulating precipitation. The rate of increase of the four water conservation indicators were lower than that of extreme precipitation indices, there will be more extreme precipitation converted into surface runoff, thereby increasing the risk of flooding. The four indicators increased in the southern and northern regions of the BJRB, while decreased in the central region during 1965–2009. (3) The four indicators were generally positively correlated with most of the extreme precipitation indices in the northern BJRB, especially RX1day, RX3day, RX5day, RX7day, R99p, and R50mm. The results provide a scientific reference for water conservation adaptation to extreme precipitation variations, protection of the ecological environment, and the prevention and reduction of disasters in the region.

Understanding flash flooding in the Himalayan Region: a case study

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

Evaluation of Multisource Datasets in Characterizing Spatiotemporal Characteristics of Extreme Precipitation from 2001 to 2019 in China

Abstract The spatiotemporal characteristics of extreme precipitation intensity are crucial for hydroclimatic studies. This study delineates the spatiotemporal distribution features of extreme precipitation intensity across China from 2001 to 2019 using the gridded daily precipitation dataset CN05.1, constructed from an observation network of over 2400 stations. Furthermore, we evaluate the reliability of 12 widely used precipitation datasets (including gauge-based, satellite retrieval, reanalysis, and fusion products) in monitoring extreme precipitation events. Our findings indicate the following: 1) CN05.1 reveals a consistent spatial distribution characterized by a decline in extreme precipitation intensity from the southeastern coastal regions toward the northwestern inland areas of China. From 2001 to 2019, more pronounced declining intensity trends are discernible in the northern and southwestern regions of China, whereas marked increasing trends manifest in the northeastern and the Yangtze River plain regions. National mean extreme precipitation indices consistently exhibit significant increasing trends throughout China. 2) Datasets based on station observations generally exhibit superior applicability concerning spatiotemporal distribution. 3) Multisource weighted precipitation fusion products effectively capture the temporal variability of extreme precipitation indices. 4) Satellite retrieval datasets exhibit notable performance disparities in representing various intensity indices. Most products tend to overestimate the increasing trends of national mean intensity indices. 5) Reanalysis datasets tend to overestimate extreme precipitation indices, and inadequately capture the trends. ERA5 and JRA-55 underestimate trends, while CFSR and MERRA-2 significantly overestimate the trends. These findings serve as a basis for selecting reliable precipitation datasets for extreme precipitation and hydrological simulation research in China. Significance Statement Extreme precipitation events have increasingly become more widespread, posing significant threats to human lives and property. Accurately understanding the spatiotemporal patterns of these events is imperative for effective mitigation. Despite the proliferation of precipitation products, their capacity to faithfully represent extreme events remains inadequately validated. In this study, we utilize a gauge-based dataset derived from over 2400 gauge stations across China to investigate the spatiotemporal changes in extreme precipitation events from 2001 to 2019. Subsequently, we conduct a rigorous evaluation of 12 widely used precipitation datasets to assess their efficacy in depicting extreme events. The results of this research offer valuable insights into the strengths and weaknesses of various precipitation products in depicting extreme events.

Attributing Extreme Precipitation Characteristics in South China Pearl River Delta Region to Anthropogenic Influences Based on Pseudo Global Warming

AbstractIn the context of human‐induced warming climate, the atmosphere is expected to hold a greater amount of water vapor, leading to heavier precipitation on a global scale. However, the extent to which changes in extreme rainfall can be attributed to human influences varies at regional scales. Here we conduct attribution analyses on 40 extreme precipitation events in different seasons during 1998–2018 over the Pearl River Delta (PRD), by using the Weather Research and Forecasting (WRF) model and applying the pseudo global warming (PGW) method. The model was integrated with the factual and counterfactual conditions separately, with the latter derived from differences between the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and historical‐natural runs. By comparing parallel experiments, extreme daily rainfall (>95th percentile) in PRD enhanced by 8%–9.5% for 0.9–1.1 K near‐surface warming (nearly Clausius‐Clapeyron, or CC scaling) in the May‐to‐September (MJJAS) and 12.4% at most for 0.6–0.8 K warming (∼2 × CC rate) in non‐MJJAS seasons; the probability of those extremes increased by 10%–30% during MJJAS (20%–40% in other seasons). While moisture‐related thermodynamic effects play a similar role in modulating rainfall, the wind circulation‐related dynamic effects act differently in different seasons. Changes in MJJAS extremes are related to stronger low‐level southerly winds, while non‐MJJAS rainfall is exacerbated by strengthened low‐level wind convergence and updrafts. Moisture budget analysis suggests that thermodynamic effects associated with the increased moisture amount account for the mean rainfall increase, whereas dynamic effects related to wind circulation changes are responsible for extreme precipitation, regardless of seasons.

Construction of daily precipitation series and the observational characteristics of extreme precipitation in Tianjin, China during 1888–2022

Given the difficulties in rescuing and ensuring the quality of long-term climate data, current studies on century-scale climate change are usually limited to annual and monthly data, resulting in the poor detection of extreme climate events and their changes before 1950. In this study, we reconstructed a daily precipitation series for Tianjin from 15 September 1887 to 31 December 2022 on the basis of the most comprehensive daily precipitation records collected from the Tianjin Meteorological Archive, China, and in reference to the precipitation analysis results based on the datasets developed by the Climatic Research Unit Time-Series version 4.06, Global Precipitation Climatology Centre and University of Delaware along with the application of various homogenisation methods for climate series. Our approach provides a complete and reliable century-long daily precipitation series for the study of regional or local extreme weather and climate events. The reconstructed daily dataset reveals that the annual precipitation amount and R95 intensity in Tianjin during 1888–2022 lack significant trends and have values of 0.74 ± 6.99 and −1.84 ± 3.22 mm per decade, respectively. On the annual and seasonal scales, the precipitation amount and R95 intensity, particularly those in autumn, have increased since the latter half of the 20th century relative to those in 1888–1950. However, the increase in precipitation amount and R95 intensity is relatively limited compared with that in atmospheric water vapour content due to surface warming, indicating the highly sensitive response of extreme precipitation events to warming. In addition, the estimates for the return periods of 5, 10, 20, 50 and 100 years covering 1888–2022, 1888–1950 and 1951–2022 depict that the intensity of heavy rain and above magnitude was highest in 1888–1950 and decreased in 1951–2022.

РАЗРАБОТКА КАРТОГРАФИЧЕСКОЙ БАЗЫ ДАННЫХ ОБ ОПАСНЫХ ГИДРОЛОГИЧЕСКИХ ЯВЛЕНИЯХ (НА ПРИМЕРЕ БАССЕЙНА Р. КАМЫ)

Databases of hazardous hydrological events (HHE) provide an information basis for their mapping and risk assessment, and are also important for assessing the reliability of modeling and forecasts. For the territory of Russia, the publicly availableinformation on HHE is incomplete and unstructured. This paper deals with the creation of a database of HHE in the Kama River basinfor the period from 1990 to the present. The compiled dataset is based on long-term observations at a network of gauging stations, onpreviously published databases of flood events, publications in the media, social networks, and specialized information resources. Thestructure of the database includes three hierarchical levels: reports on a flood event and/or related damage with geolocation at settlements or gauging stations, information on a flood event for the entire river basin, and characteristics of extreme precipitation (only forrain-induced floods). In total, the database includes 282 reports, of which 223 cases induced economic losses. The majority of cases (79%) are associated with spring floods; rain, rain-on-snow, and flash floods accounted for 17% of cases; the other 4% are related toice jams and icings. The developed database will serve as a basis for risk mapping in the Kama River basin.

Characteristics of extreme precipitation and its sensitivity to regional climate change in the upper and middle reaches of the Yellow River Basin

Characteristics of extreme precipitation and its sensitivity to regional climate change in the upper and middle reaches of the Yellow River Basin

The Principal Modes of Morning Extreme Precipitation over Inland Guangdong, China during Pre-Summer Rainy Season

The study explores the characteristics of morning extreme precipitation (MEP) during the pre-summer in inland Guangdong. Based on the principal modes, MEP events can be classified into four groups. The first group of MEP (G1) is a typical southeastward-propagating rainfall system originating from the northwestern mountains. This is caused by the strongest accelerated southwesterly winds at night, which bring abundant moist and warm air from the South China Sea (SCS) along with the shear line and the highest convective available potential energy (CAPE). The second group of MEP (G2) is warm-sector heavy rainfall with large-scale warming and higher CAPE. This local rainfall system originates in the south of Nanling mountains at night and reaches its mature stage in the morning. The rainfall system of the third group (G3) originates in central Guangxi and propagates to the southern inland region. The southeasterly winds in Guangxi intensify at night due to the anomalous cyclonic circulation. However, in the morning, the easterly winds shift to the westerlies, favoring eastward propagation. After SCS monsoon onset, cold air intrudes southward, colliding with moist warm air from the SCS, leading to heavy frontal precipitation in the inland region, classified as the fourth group MEP (G4).

Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley

Abstract Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.